$x_t=w_tw_{t-1}$, where $w_t$~$N(0,\sigma^2)$, $w_t$ uncorrelated.
$y_t=x^{2}_{t}$
$$E[y_t]=(\sigma^2)^2$$
Compute autocovariance $\gamma(h)$ of $y_t$, when $h=1$.
$$cov(y_t, y_{t+1})=cov(x^{2}_{t}, x^{2}_{t-1})$$
$$=cov(w_tw_{t-1}, w_{t+1}w_t)$$
$$=E[w_tw_{t-1}w_{t+1}w_t]-E[w_t]E[w_{t-1}]E[w_{t+1}]E[w_t]$$
This is the first term of the above,
$E[w_tw_{t-1}w_{t+1}w_t]=E[w_t]E[w_{t-1}]E[w_{t+1}]E[w_t]$
Is this wrong? why? $w_t$ are uncorrelated, I treat them as independent.
I figured it out. Because $w_t$ is correlated with itself, you can't break first $w_t$ and second $w_t$ into two separate expected values.
For instance, $$E[w_tw_t]=E[w^{2}_{t}]$$
Therefore, $$E[w_tw_{t-1}w_tw_{t-1}]=E[w^{2}_{t}]E[w^{2}_{t-1}]$$